DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-17 are pending.
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claim(s) 1-5, 7-9 and 12-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rix et al (US20230252633A1) in view of Hennig et al (US20190221314A1).
Regarding claims 1 and 17, Rix teaches a medical information processing apparatus comprising: processing circuitry configured to
acquire a medical image obtained by imaging a subject,
(Rix, "...based on an image of the subject imaged by a medical imaging method...", [0018]; "A patient is imaged with a medical imaging device (110) that provides an image or images of a region of interest in at least two dimensions. Preferably, the medical imaging device is an MRI scanner...", [0043]; acquiring a medical image of a patient (subject) using an imaging device such as an MRI)
acquire a value of a biomarker contained in a sample collected from the subject,
(Rix, "Preferably, the patient's relevant clinical data (111) is also provided, comprising any of the following: ... actual PSA level, previous PSA level, ... histopathology results obtained from tissues associated with suspected cancer, genetic tests associated with a suspected cancer...", [0044]; Hennig, "Liquid biopsies can be used for detecting the functional molecular properties of a tumor or a disease by comprehensive and quantitative molecular analysis of the patient's fluid sample (mostly blood plasma)...", [0007]; Rix teaches providing actual biomarker values like PSA. Hennig teaches that these biomarkers are acquired through liquid biopsies of a patient sample (fluid sample))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate the sample-based biomarker acquisition of Hennig into the framework of Rix in order to provide the physical sample source for the biomarker data. The combination of Rix and Hennig also teaches other enhanced capabilities.
The combination of Rix and Hennig further teaches:
determine a region of interest on the acquired medical image,
(Rix, "...processing the image to identify at least one tissue type within a region of interest of the image and determining an amount of each identified tissue type;", [0018]; "A patient is imaged with a medical imaging device (110) that provides an image or images of a region of interest...", [0043]; acquiring images of a specific region of interest and segmenting tissues within that region)
analyze the image of the determined region of interest and calculate a property of a drawn object,
(Rix, "...image or images are processed by at least one segmentation method (130) to determine an approximate segmentation of tissues observed in the scan, characterised in that the segmentation method shall identify at least one region of at least one benign tissue type and, where the segmentation method determines a risk of cancer, the segmentation method shall identify at least one region of at least one abnormal tissue type.", [0045]; "A segmentation model is applied to calculate the volume of each tissue type for each patient case and this is recorded in the table.", [0069]; analyzing the ROI via segmentation to identify regions (drawn objects) and calculate their properties, specifically the volume of each tissue type)
select a value of a biomarker associated with the calculated property of the drawn object, and
(Rix, "...each tissue type output by the segmentation method is associated with a measure of biomarker production, such as a biomarker density, ... retrieves the applicable measure of biomarker production or biomarker density, and sums these measures (accounting for the relative volume of each voxel or pixel) to determine at least one overall biomarker level indicative of the biomarker production of the imaged tissues.", [0055]; selecting/retrieving biomarker values (production densities) that are associated with the calculated tissue types and their volumes)
determine consistency between the selected value of the biomarker and the calculated property of the drawn object.
(Rix, "The invention described herein overcomes such limitations by providing embodiments that are able to account for a biomarker test result, such as PSA, by reference to medical imaging...", [0016]; "... indicating that if the condition is low-risk, a value of approximately 4.48 ng/mL would be expected, and that if the condition is high-risk, a value of approximately 6.08 ng/mL would be expected.", [0093]; "With reference to the findings illustrated in FIG. 3 , an actual biomarker test result (for example, a PSA test result) of 4.5 ng/mL or lower might be considered to be low risk, while an actual biomarker test result of 6.0 ng/mL or higher might be considered to be high risk.", [0094]; determining consistency by comparing the measured "actual" biomarker value with the expected values calculated from the image properties (tissue types and volumes) to determine if the findings are consistent with a low or high-risk state)
Regarding claim 2, the combination of Rix and Hennig teaches its/their respective base claim(s).
The combination further teaches the medical information processing apparatus according to claim 1, wherein the processing circuitry is further configured to
acquire a value of a biomarker associated with a lesion that is potentially present in the subject, and
calculate the property of the drawn object according to a type of the lesion.
(Rix, "Preferably, the patient's relevant clinical data (111) is also provided, comprising any of the following: ... actual PSA level...", [0044]; "...the segmentation method shall identify at least one region of at least one benign tissue type and, where the segmentation method determines a risk of cancer, the segmentation method shall identify at least one region of at least one abnormal tissue type.", [0045]; "A segmentation model is applied to calculate the volume of each tissue type for each patient case and this is recorded in the table.", [0069]; acquiring a biomarker value associated with a potential lesion (cancer) and calculating a property (volume) of the drawn object based on the tissue type)
Regarding claim 3, the combination of Rix and Hennig teaches its/their respective base claim(s).
The combination further teaches the medical information processing apparatus according to claim 1, wherein the processing circuitry is further configured to
be capable of determining a plurality of regions of interest on the acquired medical image, and
identify a region of interest having high consistency among the plurality of determined regions of interest.
(Rix, "...the segmentation method shall identify at least one region of at least one benign tissue type and, where the segmentation method determines a risk of cancer, the segmentation method shall identify at least one region of at least one abnormal tissue type.", [0045]; Hennig, "The course of therapy (treatment) can include ... identifying lesions that are cancerous and distinguishing those from ones that are non-cancerous...", [0059]; Rix identifies multiple regions. Hennig's logic for distinguishing cancerous from non-cancerous lesions using combined modalities would inherently identify the region most consistent with biomarkers. Incorporating Hennig's distinguishing logic into Rix would allow pinpointing the most consistent abnormal region)
Regarding claim 4, the combination of Rix and Hennig teaches its/their respective base claim(s).
The combination further teaches the medical information processing apparatus according to claim 1, wherein the processing circuitry is further configured to analyze past medical information to select a value of a biomarker associated with the property of the drawn object.
(Rix, "Preferably, the patient's relevant clinical data (111) is also provided, comprising any of the following: ... previous PSA level...", [0044]; using the subject's clinical history, specifically previous biomarker values, to inform the analysis)
Regarding claim 5, the combination of Rix and Hennig teaches its/their respective base claim(s).
The combination further teaches the medical information processing apparatus according to claim 1, wherein the processing circuitry is further configured to calculate a score indicating a possibility that the drawn object is the lesion based on the determined consistency.
(Rix, "With reference to the findings illustrated in FIG. 3 , an actual biomarker test result (for example, a PSA test result) of 4.5 ng/mL or lower might be considered to be low risk, while an actual biomarker test result of 6.0 ng/mL or higher might be considered to be high risk.", [0094]; calculating consistency by determining if findings represent "low-risk" or "high-risk", effectively a score for lesion possibility)
Regarding claim 7, the combination of Rix and Hennig teaches its/their respective base claim(s).
The combination further teaches the medical information processing apparatus according to claim 1, wherein the processing circuitry is further configured to, in a case where the acquired value of the biomarker is a value indicating a possibility that a lesion is present in the subject, determine the region of interest, and calculate the property of the drawn object.
(Rix, "Beneficially, the segmentation method may be preceded by a method of risk identification (121) that determines at least one risk of cancer being present...", [0050]; ROI determination and tissue property calculation are performed when a risk of a lesion is identified)
Regarding claim 8, the combination of Rix and Hennig teaches its/their respective base claim(s).
The combination further teaches the medical information processing apparatus according to claim 1, wherein the processing circuitry is further configured to, after the region of interest is determined, select a value of a biomarker associated with the property of the drawn object.
(Rix, "...each tissue type output by the segmentation method is associated with a measure of biomarker production, such as a biomarker density, ... retrieves the applicable measure of biomarker production or biomarker density...", [0055]; after segmenting the ROI into tissue types (drawn objects), the system selects/retrieves the associated biomarker density)
Regarding claim 9, the combination of Rix and Hennig teaches its/their respective base claim(s).
The combination further teaches the medical information processing apparatus according to claim 8, wherein the processing circuitry is further configured to, after the region of interest is determined, in a case where the acquired value of the biomarker is a value indicating a possibility that a lesion is present in the subject, select a value of a biomarker associated with the property of the drawn object.
(Rix, "...where the segmentation method determines a risk of cancer, the segmentation method shall identify at least one region of at least one abnormal tissue type.", [0045]; "...the biomarker calculation method (140) retrieves the applicable measure of biomarker production or biomarker density...", [0055]; see comments on claims 7 and 8.)
Regarding claim 12, the combination of Rix and Hennig teaches its/their respective base claim(s).
The combination further teaches the medical information processing apparatus according to claim 1, wherein the processing circuitry is further configured to determine the region of interest based on machine learning.
(Rix, "A segmentation method may be a deep learning segmentation model, U-net, V-net, radiomic method...", [0048]; using machine learning (deep learning) to segment regions of interest)
Regarding claim 13, the combination of Rix and Hennig teaches its/their respective base claim(s).
The combination further teaches the medical information processing apparatus according to claim 12, further comprising an input interface that receives an input operation for correcting the determined region of interest.
(Rix, "In an alternative embodiment, candidate tissue segmentations may be made available to a human operator for review and possible correction subsequent to step (130)...", [0064]; an interface for human correction of machine-determined ROIs)
Regarding claim 14, the combination of Rix and Hennig teaches its/their respective base claim(s).
The combination further teaches the medical information processing apparatus according to claim 1, wherein the sample collected from the subject is a body fluid of the subject.
(Hennig, "Liquid biopsies can include analysis of any body fluids (e.g., blood, urine, saliva...)", [0007]; collecting biomarker data from samples that are subject body fluids)
Regarding claim 15, the combination of Rix and Hennig teaches its/their respective base claim(s).
The combination further teaches the medical information processing apparatus according to claim 1, wherein the processing circuitry is further configured to
acquire values of a plurality of types of biomarkers, and
select a plurality of values of biomarkers from the plurality of types of biomarkers for which the values were obtained.
(Hennig, "...measuring a second plurality of quantitative and/or semi-quantitative molecular parameters from the liquid biopsy...", [0004]; acquiring a plurality of biomarker values from the sample.)
Regarding claim 16, the combination of Rix and Hennig teaches its/their respective base claim(s).
The combination further teaches the medical information processing apparatus according to claim 3, wherein the processing circuitry is further configured to output a result of estimation of information on a state of the subject based on the identified region of interest.
(Rix, "The at least one overall biomarker level is output as a result (150) and may be used for clinical interpretation ... identifying treatment response; selecting treatment; calculating a risk of cancer; calculating a prognosis.", [0056]; outputting an estimated result indicating the subject's state based on identified regions)
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rix et al (US20230252633A1) in view of Hennig et al (US20190221314A1) and further in view of Arai et al (US20200261046A1).
Regarding claim 6, the combination of Rix and Hennig teaches its/their respective base claim(s).
The combination does not expressly disclose but Arai teaches the medical information processing apparatus according to claim 5, wherein the processing circuitry is further configured to output an alert in a case where the calculated score is less than a threshold.
(Arai, "warning notification unit 57 notifies of a warning at least in a case where ... there is no calcification having the tissue similarity degree equal to or greater than the predetermined threshold", [0067]; "warning notification unit that notifies of a warning at least in a case where there is no candidate to be highlighted by the display unit.", [0013]; Rix, "actual biomarker test result (for example, a PSA test result) of 4.5 ng/mL or lower might be considered to be low risk", [0094]; Rix teaches calculating a risk state (score) based on whether biomarker findings fall below a specific threshold (e.g., 4.5 ng/mL). Arai teaches a medical apparatus with a warning notification unit (alert) configured to provide a warning when a calculated score (tissue similarity degree) fails to meet a predetermined threshold)
It would have been obvious to a person of ordinary skill in the art to incorporate the warning notification logic of Arai into the consistency determination circuitry of Rix to output an alert when the calculated risk score or similarity is less than a threshold, thereby ensuring the practitioner is automatically notified of non-conclusive or low-risk findings. The combination of Rix, Hennig and Arai also teaches other enhanced capabilities.
Claim(s) 10-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rix et al (US20230252633A1) in view of Hennig et al (US20190221314A1) and further in view of Tai (WO2023204768A1).
Regarding claim 10, the combination of Rix and Hennig teaches its/their respective base claim(s).
The combination does not expressly disclose but Tai teaches the medical information processing apparatus according to claim 9, wherein the processing circuitry is further configured to,
in a case where the acquired value of the biomarker is not a value indicating a possibility that a lesion is present in the subject, select a value of a biomarker not associated with the lesion that is potentially present in the subject, and
determine the consistency based on the value of the biomarker not associated with the selected lesion.
(Tai, "possible to determine whether the fibrosis has been progressive or regressive... a comparison of the sum of the portal fibrosis, peri portal fibrosis, peri central fibrosis, bridging fibrosis and peri sinusoidal fibrosis values after treatment against a corresponding sum before treatment can provide an indication of progression or regression.", [0055]; Rix, "the segmentation method shall identify at least one region of at least one benign tissue type", [0045]; Rix teaches identifying benign (non-lesion) tissue types. Tai teaches selecting and quantifying specific secondary biomarkers (fibrosis sub-features) to determine regression or "no change" states where a progressive lesion is absent)
It would have been obvious to a person of ordinary skill in the art to incorporate Tai's selection and evaluation of non-malignant markers into Rix's system to determine consistency for subjects where the primary biomarker does not indicate a lesion. The combination of Rix, Hennig and Tai also teaches other enhanced capabilities.
Regarding claim 11, the combination of Rix, Hennig and Tai teaches its/their respective base claim(s).
The combination further teaches the medical information processing apparatus according to claim 10, wherein the value of the biomarker not associated with the lesion includes a value of a biomarker associated with fibrosis.
(Tai, "The histopathological feature may comprise fibrosis, and the selected plurality of sub-features may comprise portal fibrosis, peri portal fibrosis, peri central fibrosis, bridging fibrosis and peri sinusoidal fibrosis.", [0009]; utilizing biomarker values specifically associated with fibrosis and its sub-types)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JIANXUN YANG whose telephone number is (571)272-9874. The examiner can normally be reached on MON-FRI: 8AM-5PM Pacific Time.
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/JIANXUN YANG/
Primary Examiner, Art Unit 2662 7/25/2026
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